Project Summary Autism spectrum disorder (ASD) is a heritable, heterogeneous neurodevelopmental disorder that is diagnosed on the basis of impairments in social communication, as well as the presence of repetitive behaviors and restricted interests. Although the disorder has a rising prevalence last estimated at 1 in 68 children, there is still a paucity of broadly effective treatments. This is in part because there is still little known about the underlying neurobiological basis of ASD. Furthermore, the considerable heterogeneity present in ASD makes it difficult to draw conclusions about how the brains of individuals with ASD differ from those of typically developing (TD) persons. This project will take an interdisciplinary approach to improve our understanding of the mechanisms underlying ASD and to parse the heterogeneity present in ASD by analyzing how common genetic variants may affect the brain. This project will use magnetic resonance imaging (MRI) to relate functional and structural connectivity to cumulative genetic risk in 80 children and adolescents with ASD (age 8-16) and 80 matched TD controls (age 8-16). Functional connectivity will be calculated from resting-state functional MRI scans by correlating activity time courses across different brain regions. Structural connectivity will be quantified through diffusion tensor imaging (DTI), which measures white matter microstructure based on the directional movement of water molecules. Cumulative genetic risk will be based on participants' genotypes in several common genetic variants across three genes which have been consistently linked to ASD (CNTNAP2, MET, OXTR). This study will also examine whether genetic risk influences the brains of individuals with ASD differently than those of TD controls; for instance, youth with ASD may be more vulnerable to the effect of such risk variants. To better understand the impact of genetic risk and the neural variation related to such vulnerability, genetic risk and associated measures of brain connectivity will be related to individual differences in ASD symptomatology. Lastly, a classifier analysis based on brain connectivity measures will be used to determine how including genetic information may improve the ability to predict diagnosis from brain measures. By examining how cumulative genetic risk impacts brain connectivity, this research may suggest one potential mechanism through which risk genes contribute to ASD and ultimately lead to more targeted treatments. This approach will also begin to characterize individual variation within ASD, which may eventually aid with the development of more personalized interventions. Lastly, current attempts to diagnose ASD using brain-based measures do not yet have a high enough accuracy for clinical use. Accounting for some of the heterogeneity in ASD may improve prediction abilities and thereby aid with quantitative early diagnosis and subsequent early interventions in younger populations.